Bulk requests will yield much better performance than single-document index
requests. In order to know the optimal size of a bulk request, you should run
a benchmark on a single node with a single shard. First try to index 100
documents at once, then 200, then 400, etc. doubling the number of documents
in a bulk request in every benchmark run. When the indexing speed starts to
plateau then you know you reached the optimal size of a bulk request for your
data. In case of tie, it is better to err in the direction of too few rather
than too many documents. Beware that too large bulk requests might put the
cluster under memory pressure when many of them are sent concurrently, so
it is advisable to avoid going beyond a couple tens of megabytes per request
even if larger requests seem to perform better.

A single thread sending bulk requests is unlikely to be able to max out the
indexing capacity of an Elasticsearch cluster. In order to use all resources
of the cluster, you should send data from multiple threads or processes. In
addition to making better use of the resources of the cluster, this should
help reduce the cost of each fsync.

Make sure to watch for TOO_MANY_REQUESTS (429) response codes
(EsRejectedExecutionException with the Java client), which is the way that
Elasticsearch tells you that it cannot keep up with the current indexing rate.
When it happens, you should pause indexing a bit before trying again, ideally
with randomized exponential backoff.

Similarly to sizing bulk requests, only testing can tell what the optimal
number of workers is. This can be tested by progressively increasing the
number of workers until either I/O or CPU is saturated on the cluster.

The operation that consists of making changes visible to search - called a
refresh - is costly, and calling it often while there is
ongoing indexing activity can hurt indexing speed.

By default, Elasticsearch runs this operation every second, but only on
indices that have received one search request or more in the last 30 seconds.
This is the optimal configuration if you have no or very little search traffic
(e.g. less than one search request every 5 minutes) and want to optimize for
indexing speed.

On the other hand, if your index experiences regular search requests, this
default behavior means that Elasticsearch will refresh your index every 1
second. If you can afford to increase the amount of time between when a document
gets indexed and when it becomes visible, increasing the
index.refresh_interval to a larger value, e.g.
30s, might help improve indexing speed.

If you need to load a large amount of data at once, you should disable refresh
by setting index.refresh_interval to -1 and set index.number_of_replicas
to 0. This will temporarily put your index at risk since the loss of any shard
will cause data loss, but at the same time indexing will be faster since
documents will be indexed only once. Once the initial loading is finished, you
can set index.refresh_interval and index.number_of_replicas back to their
original values.

When indexing a document that has an explicit id, Elasticsearch needs to check
whether a document with the same id already exists within the same shard, which
is a costly operation and gets even more costly as the index grows. By using
auto-generated ids, Elasticsearch can skip this check, which makes indexing
faster.

If indexing is I/O bound, you should investigate giving more memory to the
filesystem cache (see above) or buying faster drives. In particular SSD drives
are known to perform better than spinning disks. Always use local storage,
remote filesystems such as NFS or SMB should be avoided. Also beware of
virtualized storage such as Amazon’s Elastic Block Storage. Virtualized
storage works very well with Elasticsearch, and it is appealing since it is so
fast and simple to set up, but it is also unfortunately inherently slower on an
ongoing basis when compared to dedicated local storage. If you put an index on
EBS, be sure to use provisioned IOPS otherwise operations could be quickly
throttled.

Stripe your index across multiple SSDs by configuring a RAID 0 array. Remember
that it will increase the risk of failure since the failure of any one SSD
destroys the index. However this is typically the right tradeoff to make:
optimize single shards for maximum performance, and then add replicas across
different nodes so there’s redundancy for any node failures. You can also use
snapshot and restore to backup the index for further
insurance.

If your node is doing only heavy indexing, be sure
indices.memory.index_buffer_size is large enough to give
at most 512 MB indexing buffer per shard doing heavy indexing (beyond that
indexing performance does not typically improve). Elasticsearch takes that
setting (a percentage of the java heap or an absolute byte-size), and
uses it as a shared buffer across all active shards. Very active shards will
naturally use this buffer more than shards that are performing lightweight
indexing.

The default is 10% which is often plenty: for example, if you give the JVM
10GB of memory, it will give 1GB to the index buffer, which is enough to host
two shards that are heavily indexing.